





'''simple docstring'''


import copy
from typing import TYPE_CHECKING, Any, Mapping, Optional, OrderedDict

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto.configuration_auto import AutoConfig


if TYPE_CHECKING:
       from ... import PreTrainedTokenizerBase, TensorType

_SCREAMING_SNAKE_CASE									=				logging.get_logger(__name__)

class  lowerCAmelCase_		(		__magic_name__		):
 __lowerCamelCase    :				str								=       "vision-encoder-decoder"
 __lowerCamelCase    :				List[str]								=       True




 def __init__(						self      ,  **_lowerCAmelCase		)     ->				Union[str, Any]:
     super().__init__(**_lowerCAmelCase		)
     if "encoder" not in kwargs or "decoder" not in kwargs:
         raise ValueError(
             f'''A configuraton of type {self.model_type} cannot be instantiated because '''
             f'''not both `encoder` and `decoder` sub-configurations are passed, but only {kwargs}'''		)

     _lowerCAmelCase						      =   kwargs.pop("encoder"		)
     _lowerCAmelCase						      =   encoder_config.pop("model_type"		)
     _lowerCAmelCase						      =   kwargs.pop("decoder"		)
     _lowerCAmelCase						      =   decoder_config.pop("model_type"		)

     _lowerCAmelCase						      =   AutoConfig.for_model(_lowerCAmelCase      ,  **_lowerCAmelCase		)
     _lowerCAmelCase						      =   AutoConfig.for_model(_lowerCAmelCase      ,  **_lowerCAmelCase		)
     _lowerCAmelCase						      =   True




 @classmethod
 def 				_snake_case (						cls      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  **_lowerCAmelCase		)     ->				PretrainedConfig:
     logger.info("Setting `config.is_decoder=True` and `config.add_cross_attention=True` for decoder_config"		)
     _lowerCAmelCase						      =   True
     _lowerCAmelCase						      =   True

     return cls(encoder=encoder_config.to_dict()      ,  decoder=decoder_config.to_dict()      ,  **_lowerCAmelCase		)





 def 				_snake_case (						self		)     ->				Union[str, Any]:
     _lowerCAmelCase						      =   copy.deepcopy(self.__dict__		)
     _lowerCAmelCase						      =   self.encoder.to_dict()
     _lowerCAmelCase						      =   self.decoder.to_dict()
     _lowerCAmelCase						      =   self.__class__.model_type
     return output







class  lowerCAmelCase_		(		__magic_name__		):
 __lowerCamelCase    :				str								=       version.parse("1.11"		)




 @property
 def 				_snake_case (						self		)     ->				Mapping[str, Mapping[int, str]]:
     return OrderedDict(
         [
             ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
         ]		)




 @property
 def 				_snake_case (						self		)     ->				float:
     return 1E-4





 @property
 def 				_snake_case (						self		)     ->				Mapping[str, Mapping[int, str]]:
     return OrderedDict({"last_hidden_state": {0: "batch", 1: "encoder_sequence"}}		)







class  lowerCAmelCase_		(		__magic_name__		):




 @property
 def 				_snake_case (						self		)     ->				Mapping[str, Mapping[int, str]]:
     _lowerCAmelCase						      =   OrderedDict()
     _lowerCAmelCase						      =   {0: "batch", 1: "past_decoder_sequence + sequence"}
     _lowerCAmelCase						      =   {0: "batch", 1: "past_decoder_sequence + sequence"}
     _lowerCAmelCase						      =   {0: "batch", 1: "encoder_sequence"}

     return common_inputs





 def 				_snake_case (						self      ,  _lowerCAmelCase      ,  _lowerCAmelCase = -1      ,  _lowerCAmelCase = -1      ,  _lowerCAmelCase = False      ,  _lowerCAmelCase = None      ,  )     ->				Mapping[str, Any]:
     import torch

     _lowerCAmelCase						      =   OrderedDict()

     _lowerCAmelCase						      =   super().generate_dummy_inputs(
         _lowerCAmelCase      ,  batch_size=_lowerCAmelCase      ,  seq_length=_lowerCAmelCase      ,  is_pair=_lowerCAmelCase      ,  framework=_lowerCAmelCase		)

     _lowerCAmelCase      ,      _lowerCAmelCase						      =   dummy_input["input_ids"].shape
     _lowerCAmelCase						      =   (batch, encoder_sequence, self._config.encoder_hidden_size)
     _lowerCAmelCase						      =   dummy_input.pop("input_ids"		)
     _lowerCAmelCase						      =   dummy_input.pop("attention_mask"		)
     _lowerCAmelCase						      =   torch.zeros(_lowerCAmelCase		)

     return common_inputs







class  lowerCAmelCase_		(		__magic_name__		):




 @property
 def 				_snake_case (						self		)     ->				None:
     pass




 def 				_snake_case (						self      ,  _lowerCAmelCase		)     ->				OnnxConfig:
     return VisionEncoderDecoderEncoderOnnxConfig(_lowerCAmelCase		)





 def 				_snake_case (						self      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase = "default"		)     ->				OnnxConfig:
     _lowerCAmelCase						      =   encoder_config.hidden_size
     return VisionEncoderDecoderDecoderOnnxConfig(_lowerCAmelCase      ,  _lowerCAmelCase		)



"""simple docstring"""


def      lowerCamelCase_					(   __lowerCAmelCase							)				->   int:




						'''simple docstring'''



						if not isinstance(__lowerCAmelCase  ,       __lowerCAmelCase							):
												raise TypeError("only integers accepted as input"							)
						else:
												lowerCamelCase__   =str(abs(__lowerCAmelCase							)							)
												lowerCamelCase__   =[list(__lowerCAmelCase							) for char in range(len(__lowerCAmelCase							)							)]
												for index in range(len(__lowerCAmelCase							)							):
																		num_transpositions[index].pop(__lowerCAmelCase							)
												return max(
												    int("".join(list(__lowerCAmelCase							)							)							) for transposition in num_transpositions							)


if __name__ == "__main__":
	__import__('doctest').testmod()


